Recently,　significant　progress　has　been　made　in　consistency　regularization-based　semi-supervised　medical　image　segmentation.　Typically,　a　consistency　loss　is　applied　to　enforce　consistent　prediction　of　input　images　under　different　perturbations.　However,　most　of　the　previous　methods　missed　two　key　points:　(1)　Only　a　single　weight　is　used　to　balance　the　supervised　and　unsupervised　loss,　which　fails　to　distinguish　the　variances　across　samples.　(2)　Only　forces　the　same　pixel　to　have　similar　features　in　different　data　augmentation,　yet　ignoring　the　relationship　with　other　pixels,　which　can　serve　as　a　more　robust　supervision　signal.　To　address　these　issues,　in　this　paper,　we　propose　a　novel　framework,　which　contains　two　main　components:　Dynamic　Weight　Sampling　(DWS)　module　and　Class　Agnostic　Relationship　(CAR)　module.　Specifically,　our　method　contains　one　shared　encoder　and　two　slightly　different　decoders.　Instead　of　a　fixed　weight,　the　DWS　dynamically　adjusts　the　weights　for　each　sample　based　on　the　discrepancy　between　the　predictions　of　the　two　decoders,　balancing　the　supervised　and　unsupervised　losses.　The　greater　discrepancy　implies　that　the　sample　is　more　challenging,　and　the　prediction　is　less　reliable.　To　learn　from　a　more　reliable　target,　a　lower　weight　should　be　assigned　to　the　challenging　sample.　In　addition,　to　convey　the　relationship　between　pixels　as　supervision,　the　CAR　develops　relational　consistency　loss　on　class-agnostic　feature　regions.　Extensive　experiment　on　the　Left　Atrium　and　Pancreas-CT　dataset　shows　that　our　methods　have　achieved　state-of-the-art　results.