To　solve　the　label　scarcity　of　meibomian　gland　segmentation　in　infrared　meibography　images,　a　novel　framework　for　semi-supervised　meibomian　gland　segmentation　is　firstly　presented　in　this　paper.　Extra　mutual　feature　consistency　constraint　is　added　along　with　the　cross　pseudo　supervision　,　guiding　the　model　more　robustness　and　discriminative.　Meanwhile,　cross　uncertainty　rectification　is　introduced　to　avoid　noisy　labels,　further　improving　the　pseudo　supervision.　Experimental　results　on　an　internal　dataset　reveals　that　our　method　yields　significant　performances　using　only　10%　of　the　labeled　data　compared　to　the　fully　supervised　segmentation,　and　outperforms　the　state-of-the　art　semi-supervised　segmentation　methods.　Combination　of　mutual　consistency　regularization　and　cross　uncertainty　rectifi-cation　guides　model　to　distinguish　glands　from　background　well　with　limited　labeled　data.　©　2023　IEEE.