When using optuna I should return accuracy or loss as objective value?

I am using optuna for hyperparameter tuning for my segmentation model. At the model, I am returning accuracy as an objective value since I realised that it tries to optimize to get the best result based on the objective value. I tried the same with returning (1-loss) but I am not sure what goes with either loss or accuracy when tuning. Also for loss is there another way than 1-loss to optimize or tune based on the loss curve?