Gliomas　are　the　most　common　primary　malignant　tumors　of　the　brain.　Magnetic　resonance　(MR)　imaging　is　one　of　the　main　detection　methods　of　brain　tumors,　so　accurate　segmentation　of　brain　tumors　from　MR　images　has　important　clinical　significance　in　the　whole　process　of　diagnosis.　At　present,　most　popular　automatic　medical　image　segmentation　methods　are　based　on　deep　learning.　Many　researchers　have　developed　convolutional　neural　network　and　applied　it　to　brain　tumor　segmentation,　and　proved　superior　performance.　In　this　paper,　we　propose　a　novel　deep　learned-based　method　named　multi-scale　feature　recalibration　network(MSFR-Net),　which　can　extract　features　with　multiple　scales　and　recalibrate　them　through　the　multi-scale　feature　extraction　and　recalibration　(MSFER)　module.　In　addition,　we　improve　the　segmentation　performance　by　exploiting　cross-entropy　and　dice　loss　to　solve　the　class　imbalance　problem.　We　evaluate　our　proposed　architecture　on　the　brain　tumor　segmentation　challenges　(BraTS)　2021　test　dataset.　The　proposed　method　achieved　89.15%,　83.02%,　82.08%　dice　coefficients　for　the　whole　tumor,　tumor　core　and　enhancing　tumor,　respectively.