AIM:　To　investigate　a　pioneering　framework　for　the　segmentation　of　meibomian　glands　(MGs),　using　limited　annotations　to　reduce　the　workload　on　ophthalmologists　and　enhance　the　efficiency　of　clinical　diagnosis.　METHODS:　Totally　203　infrared　meibomian　images　from　138　patients　with　dry　eye　disease,　accompanied　by　corresponding　annotations,　were　gathered　for　the　study.　A　rectified　scribble-supervised　gland　segmentation　(RSSGS)　model,　incorporating　temporal　ensemble　prediction,　uncertainty　estimation,　and　a　transformation　equivariance　constraint,　was　introduced　to　address　constraints　imposed　by　limited　supervision　information　inherent　in　scribble　annotations.　The　viability　and　efficacy　of　the　proposed　model　were　assessed　based　on　accuracy,　intersection　over　union　(IoU),　and　dice　coefficient.　RESULTS:　Using　manual　labels　as　the　gold　standard,　RSSGS　demonstrated　outcomes　with　an　accuracy　of　93.54%,　a　dice　coefficient　of　78.02%,　and　an　IoU　of　64.18%.　Notably,　these　performance　metrics　exceed　the　current　weakly　supervised　state-of-the-art　methods　by　0.76%,　2.06%,　and　2.69%,　respectively.　Furthermore,　despite　achieving　a　substantial　80%　reduction　in　annotation　costs,　it　only　lags　behind　fully　annotated　methods　by　0.72%,　1.51%,　and　2.04%.　CONCLUSION:　An　innovative　automatic　segmentation　model　is　developed　for　MGs　in　infrared　eyelid　images,　using　scribble　annotation　for　training.　This　model　maintains　an　exceptionally　high　level　of　segmentation　accuracy　while　substantially　reducing　training　costs.　It　holds　substantial　utility　for　calculating　clinical　parameters,　thereby　greatly　enhancing　the　diagnostic　efficiency　of　ophthalmologists　in　evaluating　meibomian　gland　dysfunction.