Semi-supervised　learning　has　shown　its　effectiveness　in　the　field　of　medical　image　segmentation　tasks,　since　it　mitigates　the　heavy　burden　of　obtaining　abundant　reliable　annotations,　which　is　scarce　and　time-consuming,　by　leveraging　the　available　unlabeled　data.　In　this　paper,　we　propose　a　novel　Multi-angle　Dual-task　Consistency　Network　(MDC-Net)　for　semi-supervised　medical　image　segmentation.　Particularly,　our　MDC-Net　can　be　formulated　as　a　model　similar　to　the　Mean　Teacher　with　both　employing　exponential　moving　average　(EMA).　The　difference　is　that　we　utilize　one　of　the　networks　to　augment　unlabeled　data,　thus　generating　its　pseudo　labels　and　exploiting　MixUp　on　both　labeled　and　unlabeled　data,　whose　outputs　are　then　utilized　as　the　inputs　of　another　dual-task　network　based　on　region-based　shape　constraints　and　boundary-based　surface　mismatch.　Finally,　by　jointly　learning　pseudo　labels,　the　segmentation　probability　map　and　the　signed　distance　map　of　the　target,　our　framework　not　only　overcomes　the　problem　of　incomplete　segmentation　caused　by　noise　in　pseudo　labels,　but　also　enforces　geometric　constraints　and　makes　the　model　more　robust.　Experimental　results　on　the　public　Left　Atrium　(LA)　database　show　that　our　method　achieves　performance　gains　by　incorporating　unlabeled　data　and　outperforms　six　state-of-the-art　semi-supervised　segmentation　methods.　©　2021　ACM.