Existing model-based value expansion (MVE) methods typically leverage a world model for value estimation with a fixed rollout horizon to assist policy learning. However, a proper horizon setting is essential to world-model-based policy learning. Meanwhile, choosing an appropriate horizon value is time-consuming, especially for visual control tasks. In this article, we investigate the idea of adaptively using the model knowledge for value expansion. We propose a novel world-model-based method called dynamic-horizon MVE (DMVE) to adjust the use of the world model with adaptive rollout horizon selection. Based on the reconstruction-based technique, the raw and reconstructed images are both used to obtain multihorizon rollouts by utilizing latent imagination. Then, a horizon reliability degree detection approach is given to select appropriate horizons and obtain more accurate value estimation by the reconstructed value expansion errors. Experimental results on the mainstream benchmark visual control tasks show that DMVE outperforms all baselines in sample efficiency and final performance. In addition, experiments on the autonomous driving lane-changing task further demonstrate the scalability of our method. The codes of DMVE are available at https://github.com/JunjieWang95/dmve.