Medical　image　segmentation　is　an　essential　component　of　computer-aided　diagnosis　(CAD)　systems.　Thyroid　nodule　segmentation　using　ultrasound　images　is　a　necessary　step　for　the　early　diagnosis　of　thyroid　diseases.　An　encoder-decoder　based　deep　convolutional　neural　network　(DCNN),　like　U-Net　architecture　and　its　variants,　has　been　extensively　used　to　deal　with　medical　image　segmentation　tasks.　In　this　article,　we　propose　a　novel　N-shape　dense　fully　convolutional　neural　network　for　medical　image　segmentation,　referred　to　as　N-Net.　The　proposed　framework　is　composed　of　three　major　components:　a　multi-scale　input　layer,　an　attention　guidance　module,　and　an　innovative　stackable　dilated　convolution　(SDC)　block.　First,　we　apply　the　multi-scale　input　layer　to　construct　an　image　pyramid,　which　achieves　multi-level　receiver　field　sizes　and　obtains　rich　feature　representation.　After　that,　the　U-shape　convolutional　network　is　employed　as　the　backbone　structure.　Moreover,　we　use　the　attention　guidance　module　to　filter　the　features　before　several　skip　connections,　which　can　transfer　structural　information　from　previous　feature　maps　to　the　following　layers.　This　module　can　also　remove　noise　and　reduce　the　negative　impact　of　the　background.　Finally,　we　propose　a　stackable　dilated　convolution　(SDC)　block,　which　is　able　to　capture　deep　semantic　features　that　may　be　lost　in　bilinear　upsampling.　We　have　evaluated　the　proposed　N-Net　framework　on　a　thyroid　nodule　ultrasound　image　dataset　(called　the　TNUI-2021　dataset)　and　the　DDTI　publicly　available　dataset.　The　experimental　results　show　that　our　N-Net　model　outperforms　several　state-of-the-art　methods　in　the　thyroid　nodule　segmentation　tasks.