Objective:　Diabetic　retinopathy　is　the　leading　cause　of　vision　loss　in　working-age　adults.　Early　screening　and　diagnosis　can　help　to　facilitate　subsequent　treatment　and　prevent　vision　loss.　Deep　learning　has　been　applied　in　various　fields　of　medical　identification.　However,　current　deep　learning-based　lesion　segmentation　techniques　rely　on　a　large　amount　of　pixel-level　labeled　ground　truth　data,　which　limits　their　performance　and　application.　In　this　work,　we　present　a　weakly　supervised　deep　learning　framework　for　eye　fundus　lesion　segmentation　in　patients　with　diabetic　retinopathy.　Methods:　First,　an　efficient　segmentation　algorithm　based　on　grayscale　and　morphological　features　is　proposed　for　rapid　coarse　segmentation　of　lesions.　Then,　a　deep　learning　model　named　Residual-Attention　Unet　(RAUNet)　is　proposed　for　eye　fundus　lesion　segmentation.　Finally,　a　data　sample　of　fundus　images　with　labeled　lesions　and　unlabeled　images　with　coarse　segmentation　results　is　jointly　used　to　train　RAUNet　to　broaden　the　diversity　of　lesion　samples　and　increase　the　robustness　of　the　segmentation　model.　Results:　A　dataset　containing　582　fundus　images　with　labels　verified　by　doctors,　including　hemorrhage　(HE),　microaneurysm　(MA),　hard　exudate　(EX)　and　soft　exudate　(SE),　and　903　images　without　labels　was　used　to　evaluate　the　model.　In　ablation　test,　the　proposed　RAUNet　achieved　the　highest　intersection　over　union　(IOU)　on　the　labeled　dataset,　and　the　proposed　attention　and　residual　modules　both　improved　the　IOU　of　the　UNet　benchmark.　Using　both　the　images　labeled　by　doctors　and　the　proposed　coarse　segmentation　method,　the　weakly　supervised　framework　based　on　RAUNet　architecture　significantly　improved　the　mean　segmentation　accuracy　by　over　7%　on　the　lesions.　Significance:　This　study　demonstrates　that　combining　unlabeled　medical　images　with　coarse　segmentation　results　can　effectively　improve　the　robustness　of　the　lesion　segmentation　model　and　proposes　a　practical　framework　for　improving　the　performance　of　medical　image　segmentation　given　limited　labeled　data　samples.