Meibomian　gland　dysfunction　(MGD)　is　the　leading　cause　of　dry　eyes,　and　the　accurate　segmentation　of　meibomian　glands　in　infrared　meibography　images　is　essential　for　its　evaluation　and　diagnosis.　However,　obtaining　pixel-wise　manual　annotation　remains　a　highly　time-consuming　and　knowledge-intensive　work,　severely　hindering　the　progress　of　segmentation　technologies.　Using　weak　annotation,　such　as　scribble,　has　demonstrated　promise　in　reduction　of　annotation　cost.　But　these　weakly　supervised　methods　still　suffer　from　limited　supervision　of　sparse　annotations.　In　this　paper,　we　propose　a　novel　scribble-supervised　segmentation　model　for　meibomian　glands,　exploring　the　perturbation　and　conflict　characteristics　inherent　in　the　dual-branch　structure.　Our　model　leverages　an　additional　branch　to　induce　perturbations,　generating　pseudo-labels　by　dynamically　mixing　the　predictions　of　both　branches.　This　approach　enriches　the　supervision　information　and　mitigates　the　risk　of　convergence　to　local　optima.　Meanwhile,　an　uncertainty-based　separated　self-training　strategy　is　introduced　to　handle　conflict　prediction,　guiding　the　model　to　discern　and　extract　valuable　information　from　predictions　with　varying　confidence　levels.　Experimental　results　on　an　internal　dataset　demonstrate　that　our　approach　achieves　outstanding　performance.　It　outperforms　existing　state-of-the-art　methods,　even　with　just　30%　of　the　usual　annotation　amounts.