It seems these terms are confusing. Some experts think that these terms have a contrasting meaning which is incorrect. Is there someone who can justify the interpretation.
They are opposites. skedasis means “dispersion”, so hetero mean different variances and homo indicates same/constant variances of the distribution where the shocks/errors/disturbances come from.
For example, if some observations get their errors from the blue distribution (lower variance), while others are drawn from the red (higher variance), you have heteroskedasticity.
They are actually opposite!
Think, for instance, of a linear model where $Y=\beta_0 + \beta_1 x + \epsilon$ where $\epsilon$ is constant. Here you have homoskedasticity, since variance will always be the same, regardless of $x$
If $\epsilon$ depended on the values of $x$, you would have heteroskedasticity, with the variance being different depending on the value of the regressor