Salient　object　segmentation　has　attracted　increasing　attention　in　recent　years.　Though　a　lot　of　works　based　on　autoencoder　have　been　made,　these　methods　can　only　recognize　and　segment　one　class　of　objects,　but　cannot　segment　the　other　classes　of　objects　in　the　same　image.　To　address　this　issue,　this　paper　proposes　a　novel　autoencoder　to　perform　task-driven　object　segmentation,　in　which　a　control　signal　is　added　to　the　decoder　to　determine　which　class　of　objects　need　to　be　segmented.　What’s　more,　we　establish　a　Bayesian　rule　in　the　decoder,　in　which　the　control　signal　is　set　as　the　prior,　and　the　latent　features　learned　in　encoder　is　transferred　to　corresponding　layer　of　decoder　as　observation,　thus　the　posterior　probability　of　each　object　with　respect　to　the　specific-class　can　be　calculated,　and　the　objects　belonging　to　this　class　can　be　segmented.　This　proposed　method　is　evaluated　for　task-driven　salient　object　segmentation　on　a　several　benchmark　datasets,　including　DUT-OMRON,　ECSSD,　DUTS-TE,　etc.　Experimental　results　show　that　our　approach　tends　to　segment　accurate,　detailed　and　complete　objects,　and　improves　the　performance　compared　with　the　previous　state-of-the-arts.　©　2023,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.