Purpose: An accurate zonal segmentation of the prostate is required for prostate cancer management with MRI. Approach:The aim of this work is to present UFNet, a deep learning-based method for automatic zonal segmentation of the prostate from T2-weighted (T2w) MRI. It takes into account the image anisotropy, includes both spatial andchannel-wise attention mechanisms and uses loss functions to enforce prostate partition. The method was applied ona private multicentric 3D T2w MRI dataset and on the public 2D T2w MRI dataset ProstateX. To assess the modelperformance, the structures segmented by the algorithm on the private dataset were compared with those obtained byseven radiologists of various experience levels. Results: On the private dataset, we obtained a Dice score (DSC) of 93.90 ± 2.85 for the whole gland (WG), 91.00 ± 4.34 for the transition zone (TZ) and 79.08 ± 7.08 for the peripheral zone (PZ). Results were significantly better thanother compared networks’ (p-value<.05). On ProstateX we obtained a DSC of 90.90 ± 2.94 for WG, 86.84 ± 4.33 forTZ and 78.40 ± 7.31 for PZ. These results are similar to state-of-the art results and, on the private dataset, are coherent with those obtained by radiologists. Zonal locations and sectorial positions of lesions annotated by radiologists were also preserved. Conclusions: Deep learning-based methods can provide an accurate zonal segmentation of the prostate leading toa consistent zonal location and sectorial position of lesions, and therefore can be used as a helping tool for prostatecancer diagnosis.