  Uncertainty estimation is important for interpreting the trustworthiness of
machine learning models in many applications. This is especially critical in
the data-driven active learning setting where the goal is to achieve a certain
accuracy with minimum labeling effort. In such settings, the model learns to
select the most informative unlabeled samples for annotation based on its
estimated uncertainty. The highly uncertain predictions are assumed to be more
informative for improving model performance. In this paper, we explore
uncertainty calibration within an active learning framework for medical image
segmentation, an area where labels often are scarce. Various uncertainty
estimation methods and acquisition strategies (regions and full images) are
investigated. We observe that selecting regions to annotate instead of full
images leads to more well-calibrated models. Additionally, we experimentally
show that annotating regions can cut 50% of pixels that need to be labeled by
humans compared to annotating full images.
