  Medical image segmentation has made significant progress when a large amount
of labeled data are available. However, annotating medical image segmentation
datasets is expensive due to the requirement of professional skills.
Additionally, classes are often unevenly distributed in medical images, which
severely affects the classification performance on minority classes. To address
these problems, this paper proposes Co-Distribution Alignment (Co-DA) for
semi-supervised medical image segmentation. Specifically, Co-DA aligns marginal
predictions on unlabeled data to marginal predictions on labeled data in a
class-wise manner with two differently initialized models before using the
pseudo-labels generated by one model to supervise the other. Besides, we design
an over-expectation cross-entropy loss for filtering the unlabeled pixels to
reduce noise in their pseudo-labels. Quantitative and qualitative experiments
on three public datasets demonstrate that the proposed approach outperforms
existing state-of-the-art semi-supervised medical image segmentation methods on
both the 2D CaDIS dataset and the 3D LGE-MRI and ACDC datasets, achieving an
mIoU of 0.8515 with only 24% labeled data on CaDIS, and a Dice score of 0.8824
and 0.8773 with only 20% data on LGE-MRI and ACDC, respectively.
