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.