The　ability　to　automatically　segment　anatomical　targets　on　medical　images　is　crucial　for　clinical　diagnosis　and　interventional　therapy.　However,　supervised　learning　methods　often　require　a　large　number　of　pixel-wise　labels　that　are　difficult　to　obtain.　This　paper　proposes　a　weakly　supervised　glottis　segmentation　(WSGS)　method　for　training　end-to-end　neural　networks　using　only　point　annotations　as　training　labels.　This　method　functions　by　iteratively　generating　pseudo-labels　and　training　the　segmentation　network.　An　automatic　seeded　region　growing　(ASRG)　algorithm　is　introduced　to　generate　quality　pseudo　labels　to　diffuse　point　annotations　based　on　network　prediction　and　image　features.　Additionally,　a　novel　loss　function　based　on　the　structural　similarity　index　measure　(SSIM)　is　designed　to　enhance　boundary　segmentation.　Using　the　trained　network　as　its　core,　a　glottis　state　monitor　is　developed　to　detect　the　motion　behavior　of　the　glottis　and　assist　the　anesthesiologist.　Finally,　the　performance　of　the　proposed　approach　was　evaluated　on　two　datasets,　achieving　an　average　mIoU　and　accuracy　of　82.7%　and　91.3%.　The　proposed　monitor　was　demonstrated　to　be　effective,　which　holds　significance　in　clinical　applications.