Semi-supervised　learning　has　garnered　significant　interest　as　a　method　to　alleviate　the　burden　of　data　annotation.　Recently,　semi-supervised　medical　image　segmentation　has　garnered　significant　interest　that　can　alleviate　the　burden　of　densely　annotated　data.　Substantial　advancements　have　been　achieved　by　integrating　consistency-regularization　and　pseudo-labeling　techniques.　The　quality　of　the　pseudo-labels　is　crucial　in　this　regard.　Unreliable　pseudo-labeling　can　result　in　the　introduction　of　noise,　leading　the　model　to　converge　to　suboptimal　solutions.　To　address　this　issue,　we　propose　learning　from　reliable　pseudo-labels.　In　this　paper,　we　tackle　two　critical　questions　in　learning　from　reliable　pseudo-labels:　which　pseudo-labels　are　reliable　and　how　reliable　are　they?　Specifically,　we　conduct　a　comparative　analysis　of　two　subnetworks　to　address　both　challenges.　Initially,　we　compare　the　prediction　confidence　of　the　two　subnetworks.　A　higher　confidence　score　indicates　a　more　reliable　pseudo-label.　Subsequently,　we　utilize　intra-class　similarity　to　assess　the　reliability　of　the　pseudo-labels　to　address　the　second　challenge.　The　greater　the　intra-class　similarity　of　the　predicted　classes,　the　more　reliable　the　pseudo-label.　The　subnetwork　selectively　incorporates　knowledge　imparted　by　the　other　subnetwork　model,　contingent　on　the　reliability　of　the　pseudo　labels.　By　reducing　the　introduction　of　noise　from　unreliable　pseudo-labels,　we　are　able　to　improve　the　performance　of　segmentation.　To　demonstrate　the　superiority　of　our　approach,　we　conducted　an　extensive　set　of　experiments　on　three　datasets:　Left　Atrium,　Pancreas-CT　and　Brats-2019.　The　experimental　results　demonstrate　that　our　approach　achieves　state-of-the-art　performance.　Code　is　available　at:　https://github.com/Jiawei0o0/mutual-learning-with-reliable-pseudo-labels.