Biomedical　image　segmentation　is　one　critical　component　in　computer-aided　system　diagnosis.　However,　various　non-automatic　segmentation　methods　are　usually　designed　to　segment　target　objects　with　single-task　driven,　ignoring　the　potential　contribution　of　multi-task,　such　as　the　salient　object　detection　(SOD)　task　and　the　image　segmentation　task.　In　this　paper,　we　propose　a　novel　dual-task　framework　for　white　blood　cell　(WBC)　and　skin　lesion　(SL)　saliency　detection　and　segmentation　in　biomedical　images,　called　Saliency-CCE.　Saliency-CCE　consists　of　a　preprocessing　of　hair　removal　for　skin　lesions　images,　a　novel　colour　contextual　extractor　(CCE)　module　for　the　SOD　task　and　an　improved　adaptive　threshold　(AT)　paradigm　for　the　image　segmentation　task.　In　the　SOD　task,　we　perform　the　CCE　module　to　extract　hand-crafted　features　through　a　novel　colour　channel　volume　(CCV)　block　and　a　novel　colour　activation　mapping　(CAM)　block.　We　first　exploit　the　CCV　block　to　generate　a　target　object＇s　region　of　interest　(ROI).　After　that,　we　employ　the　CAM　block　to　yield　a　refined　salient　map　as　the　final　salient　map　from　the　extracted　ROI.　We　propose　a　novel　adaptive　threshold　(AT)　strategy　in　the　segmentation　task　to　automatically　segment　the　WBC　and　SL　from　the　final　salient　map.　We　evaluate　our　proposed　Saliency-CCE　on　the　ISIC-2016,　the　ISIC-2017,　and　the　SCISC　datasets,　which　outperform　representative　state-of-the-art　SOD　and　biomedical　image　segmentation　approaches.　Our　code　is　available　at　https://github.com/zxg3017/Saliency-CCE.