Multiple-choice question answering (MCQA) is a challenging task that requires selecting the correct answer from a set of options based on a given question. There is a trend to use pre-trained encoder-decoder models to solve MCQA. Previous works concentrate on the decoder and adopt the generated text to enhance model performance. However, few studies have optimized the use of encoders for the characteristics of MCQA. In this work, we propose a dynamic exclusion model for MCQA named ExcMC, which mimics human thinking in selection. It dynamically eliminates several incorrect options to optimize the encoder usage. ExcMC outperforms existing comparable works on two widely-used MCQA datasets, demonstrating the effectiveness of our model.