U-Net and a large number of variants of U-Net have been successfully used for liver and liver-tumor segmentation. In this paper, we propose a novel network called quartet attention U-Net (QAU-Net).First, QAU-Net employs quartet attention including four branches to capture inner and cross-dimensional interactions between channels and spatial locations. Secondly, QAU-Net employs long-short skip-connection to instead of the vanilla skip-connection, which avoids the duplicate process of low-resolution information and improves the feature fusion of low-resolution and high-resolution information. We evaluate the proposed method on the public LITS dataset. Experiments demonstrate that QAU-Net has better feature representation and higher liver and liver-tumor segmentation accuracy.