diff --git a/pytorch_lightning/trainer/connectors/callback_connector.py b/pytorch_lightning/trainer/connectors/callback_connector.py
index 0e5ba3170064a..f6a8a252b7c5b 100644
--- a/pytorch_lightning/trainer/connectors/callback_connector.py
+++ b/pytorch_lightning/trainer/connectors/callback_connector.py
@@ -150,15 +150,6 @@ def _configure_checkpoint_callbacks(self, checkpoint_callback: Optional[bool], e
             # if both are set then checkpoint only if both are True
             enable_checkpointing = checkpoint_callback and enable_checkpointing
 
-        # TODO: Remove this error in v1.5 so we rely purely on the type signature
-        if not isinstance(enable_checkpointing, bool):
-            error_msg = (
-                "Invalid type provided for `enable_checkpointing`: "
-                f"Expected bool but received {type(enable_checkpointing)}."
-            )
-            if isinstance(enable_checkpointing, Callback):
-                error_msg += " Pass callback instances to the `callbacks` argument in the Trainer constructor instead."
-            raise MisconfigurationException(error_msg)
         if self._trainer_has_checkpoint_callbacks() and enable_checkpointing is False:
             raise MisconfigurationException(
                 "Trainer was configured with `enable_checkpointing=False`"
diff --git a/tests/checkpointing/test_model_checkpoint.py b/tests/checkpointing/test_model_checkpoint.py
index 891c7c045ceaa..5513339409ab8 100644
--- a/tests/checkpointing/test_model_checkpoint.py
+++ b/tests/checkpointing/test_model_checkpoint.py
@@ -1187,12 +1187,6 @@ def test_model_checkpoint_mode_options():
         ModelCheckpoint(mode="unknown_option")
 
 
-def test_trainer_checkpoint_callback_bool(tmpdir):
-    mc = ModelCheckpoint(dirpath=tmpdir)
-    with pytest.raises(MisconfigurationException, match="Invalid type provided for `enable_checkpointing`"):
-        Trainer(enable_checkpointing=mc)
-
-
 def test_check_val_every_n_epochs_top_k_integration(tmpdir):
     model = BoringModel()
     mc = ModelCheckpoint(dirpath=tmpdir, monitor="epoch", save_top_k=-1, filename="{epoch}")