Melanoma　is　the　leading　cause　of　death　from　skin　cancer,　and　the　number　is　increasing　every　year.　However,　automated　segmentation　of　melanoma　remains　a　challenging　problem　due　to　the　great　variation　in　shape,　colour　and　texture　of　melanoma.　Moreover,　with　the　development　of　mobile　devices,　achieving　higher　performance　segmentation　on　embedded　devices　deserves　further　research.　To　address　the　above　issues,　this　paper　proposes　a　lightweight　network　for　skin　lesion　segmentation　with　guided　learning　based　on　the　attention　mechanism,　which　not　only　ensures　image　segmentation　accuracy　using　an　efficient　feature　fusion　module,　but　also　effectively　reduces　the　complexity　of　the　model.　Extensive　experiments　on　the　ISIC2017　dataset　validate　that　EGFNet　achieves　very　competitive　results　in　terms　of　objective　metrics.　©　2022　ACM.