Automated　medical　image　segmentation　is　a　crucial　step　in　clinical　analysis　and　diagnosis,　as　it　can　improve　diagnostic　efficiency　and　accuracy.　Deep　convolutional　neural　networks　(DCNNs)　have　been　widely　used　in　the　medical　field,　achieving　excellent　results.　The　high　complexity　of　medical　images　poses　a　significant　challenge　for　many　networks　in　balancing　local　and　global　information,　resulting　in　unstable　segmentation　outcomes.　To　address　the　challenge,　we　designed　a　hybrid　CNN-Transformer　network　to　capture　both　the　local　and　global　information.　More　specifically,　deep　convolutional　neural　networks　are　introduced　to　exploit　the　local　information.　At　the　same　time,　we　designed　a　trident　multi-layer　fusion　(TMF)　block　for　the　Transformer　to　fuse　contextual　information　from　higher-level　(global)　features　dynamically.　Moreover,　considering　the　inherent　characteristic　of　medical　image　segmentation　(e.g.,　irregular　shapes　and　discontinuous　boundaries),　we　developed　united　attention　(UA)　blocks　to　focus　on　important　feature　learning.　To　evaluate　the　effectiveness　of　our　proposed　approach,　we　performed　experiments　on　two　publicly　available　datasets,　ISIC-2017,　and　Kvasir-SEG,　and　compared　our　results　with　state-of-the-art　approaches.　The　experimental　results　demonstrate　the　superior　performance　of　our　approach.　The　codes　are　available　at　https://github.com/Tanghui2000/HTC-Net.