In　medical　intelligence　applications,　the　labeling　of　medical　data　is　crucial　and　expensive,　so　it　becomes　urgent　to　explore　labeling-efficient　ways　to　train　applications.　Semi-supervised　techniques　for　medical　image　segmentation　have　demonstrated　potential,　effectively　training　models　using　scarce　labeled　data　alongside　a　wealth　of　unlabeled　data.　Therefore,　semi-supervised　medical　image　segmentation　is　a　key　issue　in　engineering　applications　of　medical　intelligence.　Consistency　constraints　based　on　prototype　alignment　provide　an　intuitively　sensible　way　to　discover　valuable　insights　from　unlabeled　data　that　can　motivate　segmentation　performance.　In　this　work,　we　propose　a　Dual　prototypes　Contrastive　Network　to　motivate　semi-supervised　medical　segmentation　accuracy　by　imposing　image-level　global　prototype　and　pixel-level　local　prototype　constraints.　First,　we　introduce　a　Background-Separation　Global　Prototype　Contrastive　Learning　technique　that　utilizes　the　natural　mutual　exclusivity　of　foreground　and　background　to　separate　the　inter-class　distances　and　encourage　the　segmentation　network　to　obtain　segmentation　results　that　are　more　complete　and　do　not　contain　background　regions.　Second,　we　design　a　Cross-Consistent　Local　Prototype　Contrastive　Learning　techniques　to　extend　the　perturbation　consistency　of　the　two　networks　to　the　prototype＇s　localized　response　to　the　feature　map,　thereby　shaping　a　more　stable　intra-class　prototype　space　and　producing　accurate　and　robust　pixel-level　predictions.　Finally,　we　comprehensively　evaluate　our　method　on　mainstream　semi-supervised　medical　image　segmentation　benchmarks　and　settings,　and　experimental　results　show　that　our　proposed　method　outperforms　current　state-of-the-art　methods.　Specifically,　our　method　achieves　a　Dice　Coefficient　score　of　91.8　on　the　Automatic　Cardiac　Diagnosis　Challenge　dataset　using　only　10%　labeled　data　training,　1.1%　ahead　of　the　second　best　method.　Code　is　available　at　https://github.com/yuelily2024/DPC.