Deep learning approaches have achieved impressive results in breast ultrasound (BUS) image segmentation. However, these methods did not solve uncertainty and noise in BUS images well. To address this issue, we present a novel deep learning structure for BUS image semantic segmentation by analyzing the uncertainty using a pyramid fuzzy block and generating a novel feature based on connectedness. Firstly, feature maps in the proposed network are down-sampled to different resolutions. Fuzzy transformation and uncertainty representation are applied to each resolution to obtain the uncertainty degree on different scales. Meanwhile, the BUS images contain layer structures. From top to bottom, there are skin layer, fat layer, mammary layer, muscle layer, and background area. A spatial recurrent neural network (RNN) is utilized to calculate the connectedness between each pixel and the pixels on the four boundaries in horizontal and vertical lines. The spatial-wise context feature can introduce the characteristic of layer structure to deep neural network. Finally, the original convolutional features are combined with connectedness feature according to the uncertainty degrees. The proposed methods are applied to two datasets: a BUS image benchmark with two categories (background and tumor) and a five-category BUS image dataset with fat layer, mammary layer, muscle layer, background, and tumor. The proposed method achieves the best results on both datasets compared with eight state-of-the-art deep learning-based approaches.