How to deal with overfitting when training a model with active learning?

It seems to me that active learning will make things worse in terms of overfitting. Is it true, and how to deal with it? Here are my thoughts:

• use an annotated test set, and monitor the overfitting on that set.
• choose model and hyperparameters accordingly (=> do not allow the model to be "too" flexible)