In　medical　images,　the　edges　of　organs　are　often　blurred　and　unclear.　Existing　semi-supervised　image　segmentation　methods　rarely　model　edges　explicitly.　Thus　most　methods　produce　inaccurate　predictions　in　target　edge　regions.　In　this　paper,　we　propose　a　contour-aware　consistency　framework　for　semi-supervised　medical　image　segmentation.　The　framework　consists　of　a　shared　encoder,　a　vanilla　primary　decoder　and　a　contour-enhanced　auxiliary　decoder.　The　contour-enhanced　decoder　is　designed　to　enhance　the　features　of　the　target　contour　region.　The　predictions　from　the　primary　decoder　and　the　auxiliary　decoder　are　combined　to　create　pseudo　labels,　enabling　the　unlabeled　data　for　supervision.　For　the　inconsistent　regions　in　predictions,　we　propose　a　self-contrast　strategy　that　further　improves　the　performance　by　reducing　the　discrepancy　of　the　dual　decoder　for　the　same　pixel.　We　conducted　extensive　experiments　on　three　publicly　available　datasets　and　verified　that　our　approach　outperforms　other　methods　for　boundary　quality.　Specifically,　with　5%　labeled　data　on　Left　Atrial　(LA)　dataset,　our　proposed　approach　achieved　a　Boundary　IoU　3.76%　higher　than　the　state-of-the-art　methods.　Code　is　available　at　https://github.com/SmileJET/CAC4SSL.