diff --git a/pyproject.toml b/pyproject.toml
index 05eba62c50402..d69fb21066555 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -68,7 +68,6 @@ module = [
     "pytorch_lightning.strategies.sharded_spawn",
     "pytorch_lightning.strategies.tpu_spawn",
     "pytorch_lightning.trainer.callback_hook",
-    "pytorch_lightning.trainer.connectors.callback_connector",
     "pytorch_lightning.trainer.connectors.data_connector",
     "pytorch_lightning.trainer.supporters",
     "pytorch_lightning.trainer.trainer",
diff --git a/src/pytorch_lightning/trainer/connectors/callback_connector.py b/src/pytorch_lightning/trainer/connectors/callback_connector.py
index 83881905beeb1..bb7f912420256 100644
--- a/src/pytorch_lightning/trainer/connectors/callback_connector.py
+++ b/src/pytorch_lightning/trainer/connectors/callback_connector.py
@@ -17,6 +17,7 @@
 from datetime import timedelta
 from typing import Dict, List, Optional, Sequence, Union
 
+import pytorch_lightning as pl
 from pytorch_lightning.callbacks import (
     Callback,
     Checkpoint,
@@ -37,7 +38,7 @@
 
 
 class CallbackConnector:
-    def __init__(self, trainer):
+    def __init__(self, trainer: "pl.Trainer"):
         self.trainer = trainer
 
     def on_trainer_init(
@@ -50,7 +51,7 @@ def on_trainer_init(
         enable_model_summary: bool,
         max_time: Optional[Union[str, timedelta, Dict[str, int]]] = None,
         accumulate_grad_batches: Optional[Union[int, Dict[int, int]]] = None,
-    ):
+    ) -> None:
         # init folder paths for checkpoint + weights save callbacks
         self.trainer._default_root_dir = default_root_dir or os.getcwd()
         if weights_save_path:
@@ -95,16 +96,18 @@ def on_trainer_init(
     def _configure_accumulated_gradients(
         self, accumulate_grad_batches: Optional[Union[int, Dict[int, int]]] = None
     ) -> None:
-        grad_accum_callback = [cb for cb in self.trainer.callbacks if isinstance(cb, GradientAccumulationScheduler)]
+        grad_accum_callbacks: List[GradientAccumulationScheduler] = [
+            cb for cb in self.trainer.callbacks if isinstance(cb, GradientAccumulationScheduler)
+        ]
 
-        if grad_accum_callback:
+        if grad_accum_callbacks:
             if accumulate_grad_batches is not None:
                 raise MisconfigurationException(
                     "You have set both `accumulate_grad_batches` and passed an instance of "
                     "`GradientAccumulationScheduler` inside callbacks. Either remove `accumulate_grad_batches` "
                     "from trainer or remove `GradientAccumulationScheduler` from callbacks list."
                 )
-            grad_accum_callback = grad_accum_callback[0]
+            grad_accum_callback = grad_accum_callbacks[0]
         else:
             if accumulate_grad_batches is None:
                 accumulate_grad_batches = 1
@@ -148,6 +151,7 @@ def _configure_model_summary_callback(self, enable_model_summary: bool) -> None:
         progress_bar_callback = self.trainer.progress_bar_callback
         is_progress_bar_rich = isinstance(progress_bar_callback, RichProgressBar)
 
+        model_summary: ModelSummary
         if progress_bar_callback is not None and is_progress_bar_rich:
             model_summary = RichModelSummary()
         else:
@@ -188,7 +192,7 @@ def _configure_timer_callback(self, max_time: Optional[Union[str, timedelta, Dic
         timer = Timer(duration=max_time, interval="step")
         self.trainer.callbacks.append(timer)
 
-    def _configure_fault_tolerance_callbacks(self):
+    def _configure_fault_tolerance_callbacks(self) -> None:
         from pytorch_lightning.callbacks.fault_tolerance import _FaultToleranceCheckpoint
 
         if any(isinstance(cb, _FaultToleranceCheckpoint) for cb in self.trainer.callbacks):
@@ -196,7 +200,7 @@ def _configure_fault_tolerance_callbacks(self):
         # don't use `log_dir` to minimize the chances of failure
         self.trainer.callbacks.append(_FaultToleranceCheckpoint(dirpath=self.trainer.default_root_dir))
 
-    def _attach_model_logging_functions(self):
+    def _attach_model_logging_functions(self) -> None:
         lightning_module = self.trainer.lightning_module
         for callback in self.trainer.callbacks:
             callback.log = lightning_module.log
@@ -243,7 +247,7 @@ def _reorder_callbacks(callbacks: List[Callback]) -> List[Callback]:
             A new list in which the last elements are Checkpoint if there were any present in the
             input.
         """
-        checkpoints = [c for c in callbacks if isinstance(c, Checkpoint)]
+        checkpoints: List[Callback] = [c for c in callbacks if isinstance(c, Checkpoint)]
         not_checkpoints = [c for c in callbacks if not isinstance(c, Checkpoint)]
         return not_checkpoints + checkpoints
 
@@ -263,12 +267,12 @@ def _configure_external_callbacks() -> List[Callback]:
     else:
         from pkg_resources import iter_entry_points
 
-        factories = iter_entry_points("pytorch_lightning.callbacks_factory")
+        factories = iter_entry_points("pytorch_lightning.callbacks_factory")  # type: ignore[assignment]
 
-    external_callbacks = []
+    external_callbacks: List[Callback] = []
     for factory in factories:
         callback_factory = factory.load()
-        callbacks_list: List[Callback] = callback_factory()
+        callbacks_list: Union[List[Callback], Callback] = callback_factory()
         callbacks_list = [callbacks_list] if isinstance(callbacks_list, Callback) else callbacks_list
         _log.info(
             f"Adding {len(callbacks_list)} callbacks from entry point '{factory.name}':"