Traditional ensemble techniques such as simple majority and conventional Borda count voting have been widely applied in aggregating predictions from ensemble classifiers. This study introduces a novel preferential voting method to optimize the aggregation process by assigning exponential scores to the performance rank of each deep learning model, such as Vision Transformer (ViT). Unlike previous ensemble approaches, it aims to enhance diagnostic accuracy by tuning the ensemble hyperparameter. Using the HAM10000 skin lesion dataset, the results show that the new exponential voting method outperforms traditional voting techniques, suggesting its potential for more advanced ensemble learning for medical image classification tasks.