A high variance in the final model performance points to an unstable training routine, which might come from different data splits, the model architecture, model initialization etc.
You could try to stabilize the training for different seeds by e.g. changing the parameter initialization or making sure the data split is stratified.
You should not pick the best result based on a specific seed. If you can’t stabilize the training, you could calculate the mean and stddev of the accuracy and report this instead.