  This paper presents a simple yet effective two-stage framework for
semi-supervised medical image segmentation. Unlike prior state-of-the-art
semi-supervised segmentation methods that predominantly rely on pseudo
supervision directly on predictions, such as consistency regularization and
pseudo labeling, our key insight is to explore the feature representation
learning with labeled and unlabeled (i.e., pseudo labeled) images to regularize
a more compact and better-separated feature space, which paves the way for
low-density decision boundary learning and therefore enhances the segmentation
performance. A stage-adaptive contrastive learning method is proposed,
containing a boundary-aware contrastive loss that takes advantage of the
labeled images in the first stage, as well as a prototype-aware contrastive
loss to optimize both labeled and pseudo labeled images in the second stage. To
obtain more accurate prototype estimation, which plays a critical role in
prototype-aware contrastive learning, we present an aleatoric uncertainty-aware
method, namely AUA, to generate higher-quality pseudo labels. AUA adaptively
regularizes prediction consistency by taking advantage of image ambiguity,
which, given its significance, is under-explored by existing works. Our method
achieves the best results on three public medical image segmentation
benchmarks.
