Despite　the　success　of　deep　neural　networks　in　medical　image　classification,　the　problem　remains　challenging　as　data　annotation　is　time-consuming,　and　the　class　distribution　is　imbalanced　due　to　the　relative　scarcity　of　diseases.　To　address　this　problem,　we　propose　Class-Specific　Distribution　Alignment　(CSDA),　a　semi-supervised　learning　framework　based　on　self-training　that　is　suitable　to　learn　from　highly　imbalanced　datasets.　Specifically,　we　first　provide　a　new　perspective　to　distribution　alignment　by　considering　the　process　as　a　change　of　basis　in　the　vector　space　spanned　by　marginal　predictions,　and　then　derive　CSDA　to　capture　class-dependent　marginal　predictions　on　both　labeled　and　unlabeled　data,　in　order　to　avoid　the　bias　towards　majority　classes.　Furthermore,　we　propose　a　Variable　Condition　Queue　(VCQ)　module　to　maintain　a　proportionately　balanced　number　of　unlabeled　samples　for　each　class.　Experiments　on　three　public　datasets　HAM10000,　CheXpert　and　Kvasir　show　that　our　method　provides　competitive　performance　on　semi-supervised　skin　disease,　thoracic　disease,　and　endoscopic　image　classification　tasks.