Images captured in haze are often affected by contrast reduction, detail degradation, or color distortion, which significantly impair visual quality and affect the performance of high-level vision tasks. To effectively remove the haze from images, a multi-scale dense residual dehazing network (MDRD-Net) based on error feedback is proposed. In this network, error feedback modules (EFM) are symmetrically introduced in the encoding and decoding paths to compensate for the information loss caused by downsampling. Dense connections are introduced between error feedback modules to enhance information interaction between non-adjacent layers. To make the network focus on regions with thick haze and rich details, multiple haze aware modules (HAM) are cascaded in the feature extraction stage. Additionally, an attention mechanism is introduced in the skip connections to adaptively fuse the features from the encoder and decoder to overcome the semantic gap between deep and shallow features. Extensive experiments on the RESIDE public dataset demonstrate that the proposed method can effectively remove the haze interference and obtain clear images with true colors, high contrast, and rich details. The results, both quantitatively and qualitatively, show a significant improvement over those of many existing state-of-the-art methods.