The　sustainable　development　of　marine　fisheries　depends　on　the　accurate　measurement　of　data　on　fish　stocks.　Semantic　segmentation　methods　based　on　deep　learning　can　be　applied　to　automatically　obtain　segmentation　masks　of　fish　in　images　to　obtain　measurement　data.　However,　general　semantic　segmentation　methods　cannot　accurately　segment　fish　objects　in　underwater　images.　In　this　study,　a　Dual　Pooling-aggregated　Attention　Network　(DPANet)　to　adaptively　capture　long-range　dependencies　through　an　efficient　and　computing-friendly　manner　to　enhance　feature　representation　and　improve　segmentation　performance　is　proposed.　Specifically,　a　novel　pooling-aggregate　position　attention　module　and　a　pooling-aggregate　channel　attention　module　are　designed　to　aggregate　contexts　in　the　spatial　dimension　and　channel　dimension,　respectively.　These　two　modules　adopt　pooling　operations　along　the　channel　dimension　and　along　the　spatial　dimension　to　aggregate　information,　respectively,　thus　reducing　computational　costs.　In　these　modules,　attention　maps　are　generated　by　four　different　paths　and　are　aggregated　into　one.　The　authors　conduct　extensive　experiments　to　validate　the　effectiveness　of　the　DPANet　and　achieve　new　state-of-the-art　segmentation　performance　on　the　well-known　fish　image　dataset　DeepFish　as　well　as　on　the　underwater　image　dataset　SUIM,　achieving　a　Mean　IoU　score　of　91.08%　and　85.39%　respectively,　while　significantly　reducing　FLOPs　of　attention　modules　by　about　93%.