Semantic video segmentation is a key challenge for various applications. This paper presents a new model named Noisy-LSTM, which is trainable in an end-to-end manner, with convolutional LSTMs (ConvLSTMs) to leverage the temporal coherence in video frames, together with a simple yet effective training strategy that replaces a frame in a given video sequence with noises. Our training strategy spoils the temporal coherence in video frames and thus makes the temporal links in ConvLSTMs unreliable; this may consequently improve the ability of the model to extract features from video frames and serve as a regularizer to avoid overfitting, without requiring extra data annotations or computational costs. Experimental results demonstrate that the proposed model can achieve state-of-the-art performances on both the CityScapes and EndoVis2018 datasets. The code for the proposed method is available at https://github.com/wbw520/NoisyLSTM.