Synthetic　aperture　radar　(SAR)　image　ship　detection　has　important　applications　in　marine　surveillance.　There　are　two　limitations　when　applying　advanced　detection　methods　naively　for　SAR　ship　detection.　First,　most　detectors　construct　the　model　as　an　encoder　and　rely　on　the　feature　pyramid　network　(FPN)　head　for　accurate　prediction,　which　may　lead　to　high　computational　costs.　Second,　the　background　noises　in　the　ground　truth　(annotated　as　rectangular　bounding　boxes)　of　angular　ships　bring　difficulties　for　model　training.　To　meet　these　challenges,　we　propose　an　efficient　encoder-decoder　network　with　estimated　direction　for　ship　detection　in　SAR　images.　First,　we　present　an　anchor-free　encoder-decoder　model　that　can　efficiently　extract　multiple-level　features.　Second,　we　formulate　ship　detection　as　a　multitask　learning　problem,　including　a　bounding　box　prediction　and　a　ship　direction　regression.　The　estimated　ship　direction　can　weakly　supervise　and　benefit　ship　detection.　Furthermore,　we　develop　a　center-weighted　labeling　method　for　overlapped　annotations.　Comprehensive　experiments　on　SAR-Ship-Detection　and　SSDD　datasets　show　that　our　method　achieves　state-of-the-art　performance　with　a　high　running　speed.